Stage-wise analysis of text-based interactions

ABSTRACT

The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user&#39;s sales experience.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/149,761, filed Jan. 7, 2014, which claims priority to U.S.provisional patent application Ser. No. 61/750,729, filed Jan. 9, 2013,each of which applications are incorporated herein in their entirety bythis reference thereto.

BACKGROUND OF THE INVENTION

Technical Field

The invention relates to user relationship management. Moreparticularly, the invention relates to analyzing interactions between auser and an agent.

Description of the Background Art

Currently, users may interact with agents using a variety of channels,such as voice, chat, forums, social networks, and so on. Theseinteractions may relate to the user requesting information from theagent, where the information may be related to sales and/or service. Ina sales interaction, users approach a customer care agent with theintent of buying some commodity; whereas in service interactions, usersapproach a customer care agent for solutions to issues concerningcommodities they have already purchased. Such interactions compriseconversations between the agent and the user that are targeted towardsthe goal of solving the user's problem. These interactions may lead toconclusions, such as the agent resolving the issue and/or query of theuser, the user terminating the interaction, the agent terminating theinteraction, and so on.

Currently, only minimal analysis is performed on the data that resultsfrom these interactions. It is not presently known how to analyze andapply such interaction data effectively.

SUMMARY OF THE INVENTION

Embodiments of the invention predict the various stages of aninteraction between a potential customer (the user) and a salesrepresentative (the agent) during a sales interaction. The stages of theinteraction are predicted to understand the interaction factors thatdrive sales and, by doing so, to serve the customer better and thusincrease sales. Initially, a user makes contact with an agent via acommunications network, which may be a cellular network, a publicswitched telephone network (PSTN), a VoIP system, an IP network, and soon. During the interaction, a dropping point is reached, i.e. the pointin the interaction at which either the user or the agent ends theinteraction. The dropping point and other interaction factors areanalyzed. Based upon such analysis, various recommendations are made tothe agents to improve the user's sales experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block schematic diagram that shows an apparatus forstage-wise analysis of text-based interactions according to theinvention;

FIG. 2 is a block schematic diagram that shows an analysis engineaccording to the invention;

FIG. 3 is an agent screen that shows user drop-off at a plan enquirystage according to the invention;

FIG. 4 is an agent screen that shows an alert that is provided to theagent and a possible suggestion to the agent to avoid drop off accordingto the invention;

FIG. 5 is a block schematic diagram that shows model building andprediction logic according to the invention; and

FIG. 6 is a block schematic diagram that depicts a machine in theexemplary form of a computer system within which a set of instructionsfor causing the machine to perform any of the herein disclosedmethodologies may be executed.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention use business analysis to reveal factorsrelated to a sales process or processes, including understanding ofsales drivers for sales interactions. The various stages of aninteraction between a user and one or more agents are predicted. Thatis, the interaction is analyzed to predict the stages of the interactionbetween the user and agent, e.g. greetings, problem identification,details gathering, closure, and so on. The dropping point, i.e. thepoint in the interaction at which either the user or the agent ends theinteraction, is predicted based on prior user interactions. Based on thestage of the interaction and the prediction of the dropping point,recommendations are made to the agent to try different packages, makedifferent offerings, and so on. The recommendations serve the dualpurposes of improving the sales experience of the user, and increasingthe probability of a conversion, e.g. the user purchases the offeredgoods and/or services, by altering the interaction to avoid a droppingpoint that does not produce a conversion. Embodiments of the inventionthus predict the stages for a given interaction between a user and anagent. Such information could be further used for many business analysesrevealing many factors, such as understanding sales drivers for salesinteractions.

In embodiments of the invention, a business application analyzes thedrop-off stages for sales chats. A chat that properly finishes usuallyends with a closure as its last stage. On the other hand, a chat doesnot end successfully if either agent or customer does not close out thechat. In such cases, the last stage of the chat does not have a closure.For purposes of the discussion herein, the drop-off point for anincomplete chat is the last stage of a chat, where such stage is not aclosure. This means that chat was not successful, which indicates theintended task, e.g. a potential sale in the case of sales chats, did notoccur.

In embodiments of the invention, a large corpus of the chats ispredicted, with all the stages by using an algorithm in an offlineprocess. The chat corpus is analyzed to identify the drop-off stages atwhich users predominantly abandon the chat. These stages can then betreated specially by the agent when a current stage of the interactionduring run time that is about to be entered or that has been entered isa pre-identified drop-off stage. Analyzing stage paths, i.e. frequentstage paths that lead to these drop-off stages can also provideinformation that is used to trigger and/or alert the agent at run timeif the current interaction also follows such a path. In this way, agentscan be trained to handle such alerts and thus produce better salesresults.

FIG. 1 is a block schematic diagram that shows an apparatus forstage-wise analysis of text-based interactions according to theinvention. Embodiments of the invention that are discussed hereinconcern user management in a sales and/or service environment, althoughthose skilled in the art will appreciate that the invention has otherapplications. The apparatus shown in FIG. 1 comprises an interactionengine 104. A user 102 and an agent 103 access the interaction engine104 and interact with each other using the interaction engine 104. Theinteraction engine 104 uses any available channel, such as a cellularbased communication network, an Internet Protocol (IP) based network, apacket based communication system, a public switched telephone network(PSTN) based network, a voice over IP (VoIP) based network, and so on,as the medium of communications. The user device may be a mobile phone,a handheld device, a tablet, a computer, a telephone, or any otherdevice that is capable of communicating with the communication network102. The interaction engine 104 enables the user 102 to interact with atleast one agent 103. The mode of interaction between the agent 103 andthe user 102 may be at least one of voice-based; text-based, such aschat; social network-based; forums-based; or any other equivalent mode,or combination of modes, that allows the agent 103 and the user 102 tointeract with each other.

The analysis engine 101 captures the text of the interaction from theinteraction engine 104 through a database at the backend to whichinteraction engine 104 pushes the interaction data, i.e. chat text, foroff-line processing. At run time, analysis engine 104 directly fetchesthe data from interaction engine 101 through an API interface. Thisfacilitates the provision of runtime applications that perform drop-offanalysis, such as applications that alert an agent of a potentialdrop-off by a customer, as discussed before. As discussed below ingreater detail, the analysis engine 101 classifies the interaction intovarious stages, such as greetings, problem identification, gatheringfurther details, trouble shooting, closure, and so on. Table 1 below isan example of the various stages of a chat interaction between the agent103 and the user 102 are depicted.

TABLE 1 Stages of a Chat Interaction Chat Line Number Chat line WhoStage 1 Thank you for Agent Greetings contacting XXX Sales Chat. My nameis XXX and my Rep ID is XXX. How may I help you purchase XXX productsand services today? 2 hello I was User Problem hoping to useIdentification - one of your Promotions coupons to make a purchase butit didn't work even though it had not expired 3 I am sorry for the AgentProblem inconvenience Identification - caused. Promotions 4 it is foryour User Problem XXXPad XXX Identification - Promotions 5 May I knowthe Agent Gathering model you are Further Details - trying toConfiguration purchase? 6 XXX User Gathering Further Details -Configuration 7 Is it a XXX1 or Agent Gathering XXX2? Further Details -Configuration 8 I had a coupon User Gathering that reduces the FurtherDetails - price down to Configuration $$$, it worked last night, butwhen I tried it just now it failed; XX1 9 Please try to Agent TroubleShooting - paste Promotions XYZABC123 eCoupon and click on the “ActivateeCoupon” option present on the right side of your monitor screen 10 . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The above example is exemplary and does not restrict the interaction toa chat interaction. Those skilled in the art will appreciate that aclassification of the sort disclosed above can readily be applied to anyform of user/agent interaction, such as voice based, social network,forum, and so on.

Based on the classification of interactions into stages, the analysisengine 101 determines the dropping point for each interaction. Asdiscussed above, the dropping point for an incomplete interaction istaken to be that stage at which the interaction was ended either by thecustomer or the agent, where that stage is not a closure. For purposesof the discussion herein, the dropping point, i.e. the stage of a chat,is the point at which each interaction terminates. Based on theclassification and the determined dropping point, the analysis engine101 provides recommendations to operations and/or to the agent. Theanalysis engine 101 may also provide recommendations for interactionflow structuring and/or restructuring, such as shuffling one of thestages that results in more frequent drop-offs.

One goal is to avoid an unfavorable drop-off by predicting such stage ofa present interaction and then modifying the interaction to achieve afavorable result. This situation is illustrated in FIGS. 3 and 4, whereFIG. 3 is an agent screen that shows user drop-off at a plan enquirystage 42, and FIG. 4 is an agent screen that shows an alert 50 that isprovided to the agent and a possible suggestion to the agent to avoiddrop off.

The analysis engine 101 also provides information to agents that can bepushed to users in other forms of interaction and/or in other widgetsthe may be accessed by the user during user/agent interactions. Examplesof such widgets include, but are not limited to, slider push, alerts,etc. A slider push is typically a window with an action relevant to thecurrent interaction, where the window that is pushed to the agentconsole, and upon which the agent may choose to act.

To optimize the flow of these pushes, the analysis engine 101personalizes the interaction at the user level. This is done by miningthe Web journey that eventually leads to the interaction. For example,assume that the user browses for tablets on a vendor website, is offereda chat pop-up, and the offer is accepted. In this case, it is known thatthe user has predominantly browsed Web pages belonging to the tabletscategory. Hence, this interaction can be personalized at the user level,such that the agent is pre-informed about the user's interests, whichhelps the agent personalize the interaction. This provides an enhanceduser experience and an improvement in user engagement during theinteraction.

The analysis engine 101 also optimizes the flow based on drop-off andvisitor level during a user Web journey, as well as interaction events,such as form push, sliders, and so on. For example, from the offlineprocessing of chats it is determined that certain chat flows, i.e. stagepaths, lead to drop-off points, as discussed above. If the analysisengine 101 is equipped with such knowledge about bad chat flows, it canalert the agent when such a chat flow is detected in the currentinteraction at run time. Alternatively, certain pre-defined suggestionsand/or actions could be pushed, e.g. as a slider, to the agent console.In this way analysis engine 101 provides an offline voice of user flowrecommendations to operations and/or clients to enhance the performanceof agents and thus optimize the sales process.

Based on the above recommendation, the analysis engine 101 predicts thedrop-off point at the start the interaction and re-evaluates thedrop-off intent. Based on these models, the analysis engine 101 flashesalerts to the agents as to whether or not they should push slidersand/or forms to the user. An agent screen depicting such alert is shownin FIG. 4. The analysis engine 101 may also recommend the use of otherinformation channels, e.g. weblogs, social, chat-events, chat-journey,to make this flow much more contextual.

The analysis engine 101 also comprises a mechanism that provides forre-targeting users, once the drop-off has occurred. See copending,commonly assigned U.S. patent application Ser. No. 14/142,698, filedDec. 27, 2013 (Tracking of Near-Conversions in User Engagements), whichapplication is incorporated herein in its entirety by this referencethereto.

FIG. 2 is a block schematic diagram that shows an analysis engineaccording to the invention. In an embodiment of the invention, theanalysis engine 101 comprises a control engine 204, a transcribingmodule 202, a text mining module 203, and a database 201.

The control engine 204 receives the text of the interaction from theinteraction engine 104 via the interface 203. In an embodiment of theinvention, the control engine 204, upon receiving a recording of avoice-based interaction, sends the recording to the transcribing module202 to transcribe the recording into text. The control engine 204 labelseach line of the interaction into various stages, such as greetings,problem identification, gathering further details, trouble shooting,closure, etc., as discussed below.

The control engine 204 implements an algorithm which labels sequences oftext into stages. An embodiment of the invention uses a hidden Markovmodel (HMM) based approach to classify the interactions into stages. Ina hidden Markov model, the state, i.e. the stage, is not directlyvisible but output, i.e. the conversation between agent and customer,dependent on the state is visible. Each state has a probabilitydistribution over the possible output tokens. Therefore, the sequence oftokens generated by an HMM gives some information about the sequence ofstates. Note that the adjective ‘hidden’ refers to the state sequencethrough which the model passes, not to the parameters of the model; evenif the model parameters are known exactly, the model is still hidden.

In another embodiment of the invention, the control engine 204 consistsof a greedy sequence classifier (see below), which is an algorithm thatuses the stage predicted for a previous chat line to make a predictionfor the stage of the current chat line in the process labeling theentire conversation into stages. In another embodiment of the invention,the control engine 204 consists of a conditional random fields (CRF)algorithm (see below), which is a well-known algorithm for segmentingthe data and labeling each into stages.

Further, to distinguish between agent and customer text, the controlengine 204 learns different discriminative keywords for agent text anduser text, otherwise referred to as emission probability distribution,i.e. different emission probability distributions for the agent and theuser, and system generated text, to classify the text. This is done bylearning different probability distributions for the words that aretypically used by the agent and the user. For example, the words such as“business” and “days” occur with high probability in agent lines. Thus,by treating the emission probabilities, i.e. the chances of observing aparticular word in a particular stage, differently for the agent and theuser a better classification is performed and, hence, the stagepredictions are improved.

Based on the classifications of the interactions into stages, thecontrol engine 204 determines the dropping point for each of theinteractions. As noted above, the dropping point is the point at whicheach interaction terminates. Based on the classification and thedetermined dropping point, the control engine 204 providesrecommendations to operations and/or to the agent. The control engine204 may also provide recommendations for interaction flow structuringand/or restructuring, such as shuffling one of the stages that makesmore frequent drop-offs.

The control engine 204 also provides information to agents with regardto pushing forms and other widgets for use during the interactions. Tooptimize the flow of these pushes, the analysis engine 101 personalizesthe interaction at the user level. This provides an enhanced userexperience and an improvement in user engagement during the interaction.The control engine 204 also optimizes the flow based on drop-off andvisitor level during the user's Web journey, as well as interactionevents, such as form push, sliders, and so on.

The control engine 204 provides offline voice-of-the-user flowrecommendations to operations and/or clients to enhance the performanceof agents and to optimize the sales process. This is accomplishedthrough agent training, based upon the information that is discovered bythe control engine 201 after analyzing a large corpus of interactionsoffline. Such information is sent in the form of recommendations to theoperations center. Such information can include the particular stages atwhich agents fail to provide a good user experience, which results infrequent drop-offs by the users. This information can help train agentsto handle such situations and keep the user engaged, eventually leadingto a better user experience that converts the interaction into a sale.

Based on the above recommendation, the control engine 204 predicts thedrop-off point at the start the interaction and re-evaluates thedrop-off intent. Based on these models, the control engine 204 flashesalerts to the agents regarding whether or not the agent should pushslider and/or forms to the user. The control engine 204 may alsorecommend the use of other information channels, e.g. weblogs, social,chat-events, chat-journey, to make this flow much more contextual.

The database 201 is used for storing information, such as the text, theclassifications as performed by the control engine 204, and so on.

FIG. 5 is a block schematic diagram that shows model building andprediction logic according to the invention. In FIG. 5, an offline modelbuilding stage 34 produces a model interacts with prediction logic 41during run time.

During the model building stage, the chat database 30 is queried by asampling algorithm 31 for chats 32. The chats are processed during atagging cycle 33 in which the chats are run through a tagging team whichmanually labels each chat with stages. Iterations are performed toensure consistency in labeling. The results of the tagging cycle aretagged chats 35 which comprise the training data for model building. Thetagged chats are provided to a learning algorithm 36, discussed below.As a result, a model 37 is produced that includes state transitionprobabilities, emission probabilities, initial probabilities, andfeatures.

The model interacts with the prediction logic 41 during run time througha prediction engine 38. When a new, unlabeled chat 39 is received at theprediction engine, the chat is processed and a chat labeled with stages40 is produced.

Stage Wise Prediction of Chats Using Sequence Labeling

In the following discussion, it is assumed that training data consistingof N chats is:D={X^((i)), Y^((i)); i=1 . . . N}  (1)

where each chat line X_(j) ^((i)) at j^(th) unit of time X^((i)) istagged with stage Y_(j) ^((i)).

Three approaches to this problem are presented, namely greedy sequenceclassifier (GS), Hidden Markov Models (HMMs) and conditional randomfields (CRFs) that exploit the nature of sequence in stages. Theseapproaches are discussed below.

Greedy Sequence Classifier (GS)

This approach uses a naive Bayes classifier with sequence informationembedded. First, the stage for a chat line is predicted by using theinformation about the stage of the previous chat line. This is done asfollows:

Predict a stage Y*_(j) for any chat line X_(j) given the previous stageY*_(j−1) as follows:Y* _(j)=arg max_(Y) _(j) P(Y _(j) |X _(j) ,Y _(J−1))   (2)

Further, using the chain rule of probability:

$\begin{matrix}{\begin{matrix}{{P\left( {X_{j},Y_{j},Y_{j - 1}} \right)} = {{P\left( {{X_{j}❘Y_{j}},Y_{j - 1}} \right)}{P\left( {Y_{j}❘Y_{j - 1}} \right)}{P\left( Y_{j - 1} \right)}}} \\{= {{P\left( {{Y_{j}❘X_{j}},Y_{j - 1}} \right)}{P\left( {X_{j}❘Y_{j - 1}} \right)}{P\left( Y_{j - 1} \right)}}}\end{matrix}{{P\left( {{Y_{j}❘X_{j}},Y_{j - 1}} \right)} \propto {{P\left( {X_{j}❘Y_{j}} \right)}{P\left( {Y_{j}❘Y_{j - 1}} \right)}}}} & (3)\end{matrix}$

A stage for line X_(j) is thus predicted as:Y* _(j)=arg max_(Y) _(j) P(X _(j) |Y _(j))P(Y _(j) |Y _(j−1))   (4)

The probability P(X_(j)|Y_(j)) is called emission probability, i.e. theprobability of emitting X_(j) when in stage Y_(j). This can be estimatedfrom training data as:P(X _(j) |Y _(j))=Π_(for each word w∈x) _(j) P(w|Y _(j))   (5)

The probability P(Y_(j)|Y_(j−1)) is called state transition probability,i.e. the probability of transitioning from state Y_(j−1) to Y_(j). Theword emission (P(w|Y_(j))) and state transition probabilities can becomputed from training data.

Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs)

These are well known algorithms for labeling sequence data.

HMMs are probabilistic models for segmenting and labeling input data.HMMs model the joint probability P(Y^((i)) AND X^((i))).

CRFs are probabilistic models for segmenting and labeling input datathat directly model the conditional probability P(Y^((i))|X^((i))) whereX^((i)) is a chat segmented into stages Y^((i)).

Computer Implementation

The embodiments of the invention disclosed herein concern theoptimization of ad words based on performance across multiple channels.This allows integration of various data sources to provide a betterunderstanding of the user intent associated with user entered searchterms. The embodiments disclosed herein can be implemented through atleast one software program running on at least one hardware device andperforming network management functions to control the network elements.The network elements shown in FIGS. 1 and 2 include blocks which can beat least one of a hardware device, or a combination of hardware deviceand software module.

FIG. 6 is a block schematic diagram that depicts a machine in theexemplary form of a computer system 1600 within which a set ofinstructions for causing the machine to perform any of the hereindisclosed methodologies may be executed. In alternative embodiments, themachine may comprise or include a network router, a network switch, anetwork bridge, personal digital assistant, a cellular telephone, a Webappliance or any machine capable of executing or transmitting a sequenceof instructions that specify actions to be taken.

The computer system 1600 includes a processor 1602, a main memory 1604and a static memory 1606, which communicate with each other via a bus1608. The computer system 1600 may further include a display unit 1610,for example, a liquid crystal display (LCD). The computer system 1600also includes an alphanumeric input device 1612, for example, akeyboard; a cursor control device 1614, for example, a mouse; a diskdrive unit 1616, a signal generation device 1618, for example, aspeaker, and a network interface device 1628.

The disk drive unit 1616 includes a machine-readable medium 1624 onwhich is stored a set of executable instructions, i.e. software, 1626embodying any one, or all, of the methodologies described herein below.The software 1626 is also shown to reside, completely or at leastpartially, within the main memory 1604 and/or within the processor 1602.The software 1626 may further be transmitted or received over a network1630 by means of a network interface device 1628.

In contrast to the system 1600 discussed above, a different embodimentuses logic circuitry instead of computer-executed instructions toimplement processing entities. Other alternatives include a digitalsignal processing chip (DSP), discrete circuitry (such as resistors,capacitors, diodes, inductors, and transistors), field programmable gatearray (FPGA), programmable logic array (PLA), programmable logic device(PLD), and the like.

It is to be understood that embodiments may be used as or to supportsoftware programs or software modules executed upon some form ofprocessing core (such as the CPU of a computer) or otherwise implementedor realized upon or within a machine or computer readable medium. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine, e.g. acomputer. For example, a machine readable medium includes read-onlymemory (ROM); random access memory (RAM); magnetic disk storage media;optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals, for example, carrierwaves, infrared signals, digital signals, etc.; or any other type ofmedia suitable for storing or transmitting information.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

The invention claimed is:
 1. A computer implemented method for useranalysis, comprising: providing an interaction engine; using saidinteraction engine to capture an interaction between at least one agentand a user; providing an analysis engine; classifying, with saidanalysis engine, said interaction into one or more stages of saidinteraction; said analysis engine further implementing an algorithmwhich labels sequences of text into said stages; said analysis enginefurther implementing an algorithm that learns different discriminativekeywords for agent text and user text in said sequences of text toestablish an emission probability distribution for each of the agent andthe user in connection with classifying said sequences of text intostages; said analysis engine identifying the stage of interaction, basedon the classification predicted by the analysis engine; and based onsaid classifying, providing recommendations to said agent to improve theuser experience in the interaction.
 2. The method of claim 1, furthercomprising: using the classification predicted by the analysis enginefor offline analysis and agent performance improvement.
 3. The method ofclaim 1, further comprising: based on said classifying, predicting anyof a dropping point and recommendations leading to conversion.
 4. Themethod of claim 1, further comprising: providing said recommendations atany of a plurality of said stages.
 5. A computer implemented method forstage-wise analysis of text-based interactions between a user and atleast one agent in a sales and/or service environment, comprising:providing an interaction engine with which said user and said agentinteract with each other via any of a plurality of available channels asthe medium of communications; an analysis engine capturing text of saidinteraction from said interaction engine; said analysis engineimplementing an algorithm which labels sequences of text into saidstages; said analysis engine implementing an algorithm that learnsdifferent discriminative keywords for agent text and user text in saidsequences of text to establish an emission probability distribution foreach of the agent and the user in connection with classifying saidsequences of text into stages; based on the classification of saidinteraction into stages, said analysis engine providing recommendationsto said agent; and said analysis engine predicting the stage of apresent interaction and modifying said interaction to achieve afavorable result.
 6. The method of claim 5, wherein a mode ofinteraction between said agent and said user comprises any ofvoice-based, text-based, social network-based, and forums-based.
 7. Themethod of claim 5, wherein said channel comprises any of a cellularbased communication network, an Internet Protocol (IP) based network, apacket based communication system, a public switched telephone network(PSTN) based network, and a voice over IP (VoIP) based network.
 8. Themethod of claim 5, wherein said user interacts via a device thatcomprises any of a mobile phone, a handheld device, a tablet, acomputer, and a telephone.
 9. The method of claim 5, further comprising:said analysis engine providing information to said agent that is pushedto said user in other forms of interaction and/or in one or more widgetsthe may be accessed by the user during said user/agent interaction. 10.The method of claim 5, further comprising: said analysis enginepersonalizing said interaction at the user level.
 11. The method ofclaim 5, further comprising: said analysis engine optimizing flow basedon any of drop-off, visitor level during a user Web journey, andinteraction events.
 12. The method of claim 5, further comprising: basedon said recommendation, said analysis engine predicting drop-off pointat the start said interaction and re-evaluating the drop-off intent. 13.The method of claim 12, further comprising: said analysis engine sendingalerts to said agent to advise said agent whether or not sliders and/orforms should be pushed to the user.
 14. The method of claim 12, furthercomprising: said analysis engine recommending at least one channel otherthan a current channel as a mode of communication.
 15. The method ofclaim 5, further comprising: said analysis engine re-targeting users,once a drop-off has occurred.
 16. The method of claim 5, furthercomprising: determining dropping point for said interaction, wherein thedropping point corresponds to a stage of the interaction at which theinteraction terminates; and based on the classification and thedetermined dropping point, said analysis engine making saidrecommendations.
 17. A method for analyzing interactions between a userand at least one agent, comprising: providing a control engine forreceiving text of the interaction from an interaction engine via aninterface; said control engine sending a recording to a transcribingmodule to transcribe said recording into text upon receiving a recordingof a voice-based interaction; said control engine implementing analgorithm which labels sequences of text into said stages; said controlengine implementing an algorithm that learns different discriminativekeywords for agent text and user text in said sequences of text toestablish an emission probability distribution for each of the agent andthe user in connection with classifying said sequences of text intostages; based on the classifications of the interactions into stages,said control engine configured for determining a stage for each of aplurality of interactions; based on the classification and thedetermined stage, said control engine configured for providingrecommendations to said agent.
 18. The method of claim 17, said controlengine implementing a hidden Markov model (HMM) based approach toclassify said interactions into stages.
 19. The method of claim 17,further comprising: to distinguish between said agent and said usertext, said control engine learning different discriminative keywords foragent text and user text to classify said text.
 20. The method of claim17, further comprising: said control engine providing recommendationsfor interaction flow structuring and/or restructuring.
 21. An apparatusfor user analysis, comprising: an interaction engine for capturing aninteraction between at least one agent and a user; and an analysisengine for classifying, with said analysis engine, said interaction intoone or more stages of said interaction; said analysis engine furthercomprising an algorithm which labels sequences of text into said stages;said analysis engine further comprising an algorithm that learnsdifferent discriminative keywords for agent text and user text in saidsequences of text to establish an emission probability distribution foreach of the agent and the user in connection with classifying saidsequences of text into stages; said analysis engine identifying thestage of interaction, based on the classification predicted by theanalysis engine; and said analysis engine providing recommendations tosaid agent to improve the user experience in the interaction, based onsaid classifying.